Traditional monitoring tells you whether a system is alive. AI observability tells you whether the outputs are still useful.
Watch three classes of signal
Engineering teams need visibility into:
- system health, such as latency and failures
- output quality, such as task success or evaluator scores
- human trust, such as override rate and escalation volume
If one class is missing, the team will misunderstand incidents. A system can be technically healthy while producing business-poor outcomes.
Instrument the decision boundary
The most valuable logs often capture why the system stopped short of automation:
- confidence below threshold
- safety or policy trigger
- missing upstream context
- fallback model usage
Those signals show where reliability work should happen next.
Tie observability to release review
Observability matters most when it is used to approve, pause, or roll back a release. Otherwise teams collect traces they never translate into action.